A Forgetting Memristive Spiking Neural Network for Pavlov Experiment

Abstract

In this paper, we designed a memristive spiking neural network (MSNN) to perform a fully functional Pavlov experiment. A memristor with forgetting effect is adopted to implement synapses while Izhikevich neurons are used for generating tonic spiking and tonic bursting signals. An asymmetric linear spiking time dependent plasticity (STDP) is naturally formed by taking into account the activation time difference between pre-synaptic neurons. Our design realizes associative, correcting, and forgetting processes without learning rule control modules. Moreover, an association will be enhanced if the activation time of two neurons is close enough, otherwise, all conditioned reflex associations will be weakened.